Robust Multi-task Regression with Grossly Corrupted Observations

Huan Xu, Chenlei Leng
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1341-1349, 2012.

Abstract

We consider the multiple-response regression problem, where the response is subject to *sparse gross errors*, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-xu12b, title = {Robust Multi-task Regression with Grossly Corrupted Observations}, author = {Xu, Huan and Leng, Chenlei}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1341--1349}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/xu12b/xu12b.pdf}, url = {https://proceedings.mlr.press/v22/xu12b.html}, abstract = {We consider the multiple-response regression problem, where the response is subject to *sparse gross errors*, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors.} }
Endnote
%0 Conference Paper %T Robust Multi-task Regression with Grossly Corrupted Observations %A Huan Xu %A Chenlei Leng %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-xu12b %I PMLR %P 1341--1349 %U https://proceedings.mlr.press/v22/xu12b.html %V 22 %X We consider the multiple-response regression problem, where the response is subject to *sparse gross errors*, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors.
RIS
TY - CPAPER TI - Robust Multi-task Regression with Grossly Corrupted Observations AU - Huan Xu AU - Chenlei Leng BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-xu12b PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1341 EP - 1349 L1 - http://proceedings.mlr.press/v22/xu12b/xu12b.pdf UR - https://proceedings.mlr.press/v22/xu12b.html AB - We consider the multiple-response regression problem, where the response is subject to *sparse gross errors*, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors. ER -
APA
Xu, H. & Leng, C.. (2012). Robust Multi-task Regression with Grossly Corrupted Observations. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:1341-1349 Available from https://proceedings.mlr.press/v22/xu12b.html.

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